Proximate and Ultimate Causes for Publication

I am presently reading Steven Pinker’s “Blank Slate”. He introduces the terms “proximate cause” and “ultimate cause,” a distinction I find enlightening:

“The difference between the mechanisms that impel organisms to behave in real time and the mechanisms that shaped the design of the organism over evolutionary time is important enough to merit some jargon. A proximate cause of behavior is the mechanism that pushes behavior buttons in real time, such as the hunger and lust that impel people to eat and have sex. An ultimate cause is the adaptive rationale that led the proximate cause to evolve, such as the need for nutrition and reproduction that gave us the drives of hunger and lust.” ~Steven Pinker, The Blank Slate: The Modern Denial of Human Nature, p 54.

It is the same distinction I have made in an entirely different context between “primary” and “secondary” goals, my context being the use of measures for scientific success. In Pinker’s terminology then, enhancing our understanding of nature is the “ultimate cause” of scientific research. Striving to excel according to some measure for scientific success – like the h-factor, or the impact factor of journals on one’s publication list – is a “proximate cause”.

The comparison to evolution illuminates the problem with introducing measures for scientific success. Humans do not, in practice, evaluate each of their action as to their contribution to the ultimate cause. They use instead readily available simplifications that previously proved to be correlated with the ultimate cause. Alas, over time the proximate cause might no longer lead toward the ultimate cause. Increasing the output of publications does no more contribute to our understanding of nature than does deep-fried butter on a stick contribute to health and chances of survival.

There is an interesting opinion piece, “Impacting our young” in the Proceedings of the National Academy of Sciences of the USA (ht Jorge) that reflects on the impact that the use of measures for scientific success has on the behavior of researchers:

“Today, the impact factor is often used as a proxy for the prestige of the journal. This proxy is convenient for those wishing to assess young scientists across fields, because it does not require knowledge of the reputation of individual journals or specific expertise in all fields… [T]he impact factor has become a formal part of the evaluation process for job candidates and promotions in many countries, with both salutatory and pernicious consequences.

Not surprisingly, the journals with the highest impact factor (leaving aside the review journals) are those that place the highest premium on perceived novelty and significance. This can distort decisions on how to undertake a scientific project. Many, if not most, important scientific findings come from serendipitous discovery. New knowledge is new precisely because it was unanticipated. Consequently, it is hard to predict which projects are going to generate useful and informative data that will add to our body of knowledge and which will generate that homerun finding. Today, too many of our postdocs believe that getting a paper into a prestigious journal is more important to their career than doing the science itself.”

In other words, the proximate cause of trying to publish in a high impact journal erodes the ultimate cause of doing good science.

Another example for a proxy that distracts from recognizing good science is paying too much attention to research coming out of highly ranked universities, “highly ranked” according to some measure. This case was recently eloquently made in Nature by Keith Weaver in a piece titled “Scientists are Snobs” (sorry, subscription only):

“We all do it. Pressed for time at a meeting, you can only scan the presented abstracts and make snap judgments about what you are going to see. Ideally, these judgments would be based purely on what material is of most scientific interest to you. Instead, we often use other criteria, such as the name of the researchers presenting or their institution. I do it too, passing over abstracts that are more relevant to my work in favor of studies from star universities such as Stanford in California or Harvard in Massachusetts because I assume that these places produce the “best” science…

Such snobbery arises from a preconceived idea that many scientists have –that people end up at smaller institutions because their science has less impact or is of lower quality than that from larger places. But many scientists choose smaller institutions for quality-of-life reasons…”

He goes on to explain how his laboratory was the first to publish a scientific finding, but “recent papers… cited only a more recent study from a large US National Institutes of Health laboratory. Losing this and other worthy citations could ultimately affect my ability to get promoted and attain grants.”

In other words, using the reputation of institutions as a proxy for scientific quality does not benefit the ultimate goal of doing good science.

Now let us contrast these problems with what we can read in another recent Nature article “Beyond the Paper” by Jason Priem. He wipes away such concerns as follows:

“[A] criticism is that the very idea of quantifying scientific impact is misguided. This really will not do. We scientists routinely search out numerical data to explain everything from subatomic physics to the appreciation of Mozart; we cannot then insist that our cogitations are uniquely exempt. The ultimate judge of scientific quality is the scientific community; its judgements are expressed in actions and these actions may be measured. The only thing to do is to find good measures to replace the slow, clumsy and misleading ones we rely on today. The great migration of scholarship to the Web promises to help us to do this.”

This argument implicitly assumes that making use of a quantifiable measure for scientific impact does not affect the judgement of scientists. But we have all reason to believe it does because it replaces the ultimate cause with a proximate cause, the primary goal with a secondary goal. (Priem's article is otherwise very interesting and readable, I recommend you give it a closer look.)

I’m not against using measures for scientific success in principle. But I wish people would pay more attention to the backreaction that comes from doing the measurements and providing people with a time-saving simplified substitute for the ultimate goal of doing good science.